Temperature distribution estimation device

Abstract

To provide a temperature distribution estimation device with which it is possible to improve accountability.SOLUTION: A temperature distribution estimation device 1 comprises: a multiple regression inference unit 11 which, designed to estimate the temperature distribution of a battery cell, enters input data 1a to a multiple regression expression obtained by a multiple regression analysis and estimates the maximum temperature and minimum temperature of the battery cell; a DNN inference unit 12 having been trained using a large number of temperature distribution images of the battery cell and capable of outputting the normalized value of temperature distribution of the battery cell; a temperature distribution calculation unit 13 for calculating the temperature distribution of the battery cell on the basis of the output of the multiple regression inference unit 11 and the output of the DNN inference unit 12; and a ground explanation unit 14 for determining an item whose degree of contribution to the estimation result of temperature distribution is high on the basis of the standard partial regression coefficient obtained by a multiple regression analysis, and configured to be capable of presenting the item whose degree of contribution is high to a user.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a temperature distribution estimation device. [Background technology]

[0002] Explainable artificial intelligence is known in the art (see, for example, Patent Document 1).

[0003] The xAI platform of Patent Document 1 is configured to extract a region of interest from a whole slide image and assign a diagnostic label to the region of interest. The xAI platform stores explanatory information related to the assignment of the diagnostic label and is configured to be able to present the explanatory information to a user. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Special Publication No. 2022-527240 Summary of the Invention [Problem to be solved by the invention]

[0005] Here, in the above-mentioned explainable artificial intelligence, there is room for improvement in terms of improving explainability.

[0006] The present invention has been made to solve the above-mentioned problems, and an object of the present invention is to provide a temperature distribution estimation device that is capable of improving explainability. [Means for solving the problem]

[0007] The temperature distribution estimation device according to the present invention estimates the temperature distribution of an object, and includes a statistical inference unit that estimates the maximum and minimum temperatures of the object by substituting input data into the multiple regression equation obtained by multiple regression analysis, a learning inference unit that learns using a large number of temperature distribution images of the object and is capable of outputting normalized values ​​of the temperature distribution of the object, a temperature distribution calculation unit that calculates the temperature distribution of the object based on the output of the statistical inference unit and the output of the learning inference unit, and a basis explanation unit that is configured to determine items that contribute highly to the estimated result of the temperature distribution based on the standard partial regression coefficient obtained by the multiple regression analysis, and to present the items with high contributions to a user.

[0008] With this configuration, the maximum and minimum temperatures of the object are estimated by a statistical analysis method, and a normalized value of the temperature distribution of the object is output by a machine learning method. Then, the temperature distribution calculation unit returns the normalized value using the maximum and minimum temperatures of the object to temperature. This makes it possible to estimate the temperature distribution of the object. Furthermore, the basis explanation unit can present items with high contribution rates to the user based on the standard partial regression coefficients. Effect of the Invention

[0009] According to the temperature distribution estimation device of the present invention, it is possible to improve explainability. [Brief description of the drawings]

[0010] [Figure 1] 1 is a functional block diagram for explaining a temperature distribution estimation device of the present embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Hereinafter, one embodiment of the present invention will be described.

[0012] First, the configuration of a temperature distribution estimation device 1 according to the present embodiment will be described with reference to FIG.

[0013] The temperature distribution estimation device 1 is mounted on a vehicle, for example, and configured to estimate the temperature distribution of a battery cell in a vehicle battery pack (not shown). In other words, the temperature distribution estimation device 1 is a virtual sensor that estimates the temperature of multiple points of the battery cell. The temperature distribution estimation device 1 is configured to estimate the temperature distribution of the battery cell based on input data 1a, and display the estimation result on a display 2. The battery cell is an example of the "target object" of the present invention.

[0014] 1, the temperature distribution estimation device 1 includes a multiple regression inference unit 11, a DNN inference unit 12, a temperature distribution calculation unit 13, and a basis explanation unit 14. For example, the temperature distribution estimation device 1 is provided with a calculation unit and a storage unit (not shown), and the multiple regression inference unit 11, the DNN inference unit 12, the temperature distribution calculation unit 13, and the basis explanation unit 14 are realized by the calculation unit executing a program stored in the storage unit.

[0015] The multiple regression inference unit 11 is configured to estimate the maximum and minimum temperatures of the battery cells based on the input data 1a. The input data 1a includes various information when the vehicle is traveling, such as the vehicle speed, the pump output of the battery cooling circuit, and the SOC (State of Charge). The multiple regression inference unit 11 is an example of the "statistical inference unit" of the present invention.

[0016] Specifically, the multiple regression inference unit 11 is configured to estimate the maximum and minimum temperatures of the battery cell by substituting the input data 1a into the multiple regression equation obtained by the multiple regression analysis. For example, the maximum temperature Tmax of the battery cell is calculated using the following multiple regression equation (1), and the minimum temperature Tmin of the battery cell is calculated using the following multiple regression equation (2).

[0017] Tmax=β a0 +β a1 x1+β a2 x2+β a3 x3 (1) Tmin=β b0 +β b1 x1+βb2 x2+β b3 x3 (2) In the multiple regression equation (1), Tmax is the response variable, x1 to x3 are explanatory variables, x1 is the vehicle speed, x2 is the pump output of the battery cooling circuit, and x3 is the SOC. a0 ~ a3 is a partial regression coefficient and is previously obtained by the least squares method. In the multiple regression equation (2), Tmin is the objective variable, x1 to x3 are explanatory variables, x1 is the vehicle speed, x2 is the pump output of the battery cooling circuit, and x3 is the SOC. β b0 ~ b3 is a partial regression coefficient, which is previously obtained by the least squares method.

[0018] In addition, the standard partial regression coefficient, which is an index representing the importance of the explanatory variables in the multiple regression equation (1), is calculated in advance, and the standard partial regression coefficient, which is an index representing the importance of the explanatory variables in the multiple regression equation (2), is calculated in advance.

[0019] The DNN inference unit 12 is configured to learn using a large number of temperature distribution images of the battery cell and to be able to output a normalized value of the temperature distribution of the battery cell. The DNN inference unit 12 is configured to perform learning and inference using a deep neural network. The DNN inference unit 12 is an example of the "learning and inference unit" of the present invention.

[0020] Specifically, the DNN inference unit 12 is configured to output a normalized value of a position when position information (three-dimensional coordinate position, i.e., X-axis value, Y-axis value, and Z-axis value) of a battery cell is input. For example, position information of an area of ​​the battery cell displayed on the display 2 is input to the DNN inference unit 12, and normalized values ​​of each position of the area are output from the DNN inference unit 12. Note that the area of ​​the battery cell displayed on the display 2 can be specified by the user, for example.

[0021] The temperature distribution calculation unit 13 is configured to calculate the temperature distribution of the battery cells based on the output of the multiple regression inference unit 11 and the output of the DNN inference unit 12. That is, the temperature distribution calculation unit 13 is provided to convert the normalized value back to temperature (°C) using the maximum temperature Tmax and minimum temperature Tmin of the battery cells. This process of converting back to temperature is performed for each position input from the DNN inference unit 12, making it possible to obtain the temperature distribution of the battery cells.

[0022] For example, if the maximum temperature Tmax is 40° C. and the minimum temperature Tmin is 20° C., when the normalized value at the predetermined position is 0.5, the temperature at the predetermined position is calculated to be 30° C. Then, the temperature distribution calculation unit 13 is configured to output the calculated temperature distribution of the battery cells to the display 2 for display.

[0023] The basis explanation unit 14 is provided to improve the explainability of the estimation result of the temperature distribution. The standard partial regression coefficients of the multiple regression equations (1) and (2) are input to the basis explanation unit 14, and it is possible to determine items that contribute highly to the estimation result of the temperature distribution based on the standard partial regression coefficients. Note that explanatory variables with large standard partial regression coefficients are determined to be items that contribute highly.

[0024] The basis explanation unit 14 is configured to display a basis explanation screen (not shown) on the display 2 when the user requests the basis for the temperature distribution estimation result. The basis explanation screen lists items that have a high contribution to the temperature distribution estimation result. Therefore, by displaying the basis explanation screen, it is possible to present the items that have a high contribution to the temperature distribution estimation result to the user.

[0025] The display 2 is capable of displaying the temperature distribution of the battery cells, and is configured to be capable of displaying a reason explanation screen in response to a user request.

[0026] -Operation- Next, the operation of the temperature distribution estimation device 1 according to this embodiment will be described.

[0027] First, input data 1a is input to the multiple regression inference unit 11. The input data 1a includes vehicle speed, pump output of the battery cooling circuit, and SOC. The multiple regression inference unit 11 calculates the maximum temperature Tmax of the battery cell based on the input data 1a using the above-mentioned multiple regression equation (1), and calculates the minimum temperature Tmin of the battery cell based on the input data 1a using the above-mentioned multiple regression equation (2). The calculation results in the multiple regression inference unit 11 are output to the temperature distribution calculation unit 13.

[0028] Moreover, position information of the area displayed on the display 2 is input to the DNN inference unit 12. The DNN inference unit 12 calculates normalized values ​​of each position of the area displayed on the display 2. The calculation result in the DNN inference unit 12 is output to the temperature distribution calculation unit 13.

[0029] Then, the temperature distribution calculation unit 13 calculates the temperature distribution of the battery cells based on the input from the multiple regression inference unit 11 and the input from the DNN inference unit 12. That is, the normalized value of each position is converted back to temperature using the maximum temperature Tmax and minimum temperature Tmin of the battery cells. That is, the normalized value of each position is converted back to a temperature scale (unit). The calculation result (temperature distribution of the battery cells) in the temperature distribution calculation unit 13 is output to the display 2.

[0030] The display 2 displays the temperature distribution of the battery cells estimated by the temperature distribution estimation device 1. This temperature distribution of the battery cells is displayed in the form of a heat map, for example. In other words, temperature information of multiple points of the battery cells is displayed simultaneously.

[0031] By repeating the above-mentioned operation, the user can easily grasp the change over time in the temperature distribution of the battery cell. Also, by changing the area of ​​the battery cell displayed on the display 2 (the position information input to the DNN inference unit 12), the user can grasp the three-dimensional temperature distribution of the battery cell.

[0032] Here, when the user requests the basis for the estimation result of the temperature distribution, the basis explanation unit 14 displays a basis explanation screen on the display 2. For example, when the standard partial regression coefficient of the vehicle speed is large, the vehicle speed is displayed on the basis explanation screen as an item that contributes highly to the estimation result of the temperature distribution. Note that any number of items may be listed on the basis explanation screen, and when multiple items are listed, they may be arranged in descending order of contribution.

[0033] -effect- In this embodiment, as described above, there are provided a multiple regression inference unit 11 that estimates the maximum and minimum temperatures of the battery cells, a DNN inference unit 12 that can output a normalized value of the temperature distribution of the battery cells, and a temperature distribution calculation unit 13 that uses the maximum and minimum temperatures of the battery cells to return the normalized value to temperature. By configuring in this way, it is possible to estimate the temperature distribution of the battery cells and to speed up the estimation.

[0034] In this embodiment, a basis explanation unit 14 is provided to display a basis explanation screen on the display 2, and items with high contributions to the estimation result are displayed on the basis explanation screen, thereby improving explainability. Note that items with high contributions can be presented to the user based on the standard partial regression coefficient, and explanations are possible even if explanation information is not stored in advance, making it possible to deal with unknown problems.

[0035] -Other embodiments- It should be noted that the embodiments disclosed herein are illustrative in all respects and are not intended to be limiting. Therefore, the technical scope of the present invention is not interpreted solely by the above-described embodiments, but is defined by the claims. The technical scope of the present invention includes all modifications within the scope and meaning equivalent to the claims.

[0036] For example, in the above embodiment, a battery cell is shown as an example of the object of the present invention, but the present invention is not limited to this, and the object of the present invention may be something other than a battery cell.

[0037] Further, in the above embodiment, an example was shown in which the estimated temperature distribution was displayed on the display 2, but the present invention is not limited to this, and the estimated temperature distribution may be used for controlling the vehicle, for example.

[0038] In addition, in the above embodiment, an example was shown in which the position information of the area displayed on the display 2 is input to the DNN inference unit 12, but this is not limited to this, and all position information of the battery cells may be input to the DNN inference unit.

[0039] In addition, in the above embodiment, an example was shown in which the vehicle speed, the pump output of the battery cooling circuit, and the SOC are included in the input data 1a, but this is not limiting, and other parameters related to the temperature of the battery cell may be included in the input data.

[0040] In the above embodiment, the maximum and minimum temperatures of the battery cells are estimated by multiple regression analysis, but the present invention is not limited to this. The maximum and minimum temperatures of the battery cells may be estimated by a statistical analysis other than multiple regression analysis. For example, in the case of Lasso regression, the contribution degree can be explained from the magnitude of the regression coefficient.

[0041] In addition, in the above embodiment, an example was shown in which a normalized value of the temperature distribution of the battery cell is output using a deep neural network, but this is not limited to the above, and a normalized value of the temperature distribution of the battery cell may be output using a machine learning algorithm other than a deep neural network. [Explanation of symbols]

[0042] 1...Temperature distribution estimation device, 1a...Input data, 11...Multiple regression inference unit (statistical inference unit), 12...DNN inference unit (learning inference unit), 13...Temperature distribution calculation unit, 14...Basis explanation unit

Claims

[Claim 1] A temperature distribution estimation device that estimates a temperature distribution of an object, comprising: a statistical inference unit that estimates the maximum temperature and the minimum temperature of the object by substituting input data into a multiple regression equation obtained by multiple regression analysis; a learning and inference unit that is trained using a large number of temperature distribution images of the object and is capable of outputting a normalized value of the temperature distribution of the object; a temperature distribution calculation unit that calculates a temperature distribution of the object based on an output of the statistical inference unit and an output of the learning inference unit; A temperature distribution estimation device comprising: a basis explanation unit configured to determine items that contribute highly to a temperature distribution estimation result based on standard partial regression coefficients obtained by multiple regression analysis, and to present the items that contribute highly to a user.

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